Affiliations: [a]
Department of Biostatistics, University of Florida, Gainesville, FL, USA
| [b]
School of Community Health Sciences, University of Nevada, Reno, NV, USA
Correspondence:
[*]
Correspondence to: Guogen Shan, Department of Biostatistics, University of Florida, Gainesville, FL 32610, USA. E-mail: [email protected].
Abstract: Background:Mild cognitive impairment (MCI) patients are at a high risk of developing Alzheimer’s disease and related dementias (ADRD) at an estimated annual rate above 10%. It is clinically and practically important to accurately predict MCI-to-dementia conversion time. Objective:It is clinically and practically important to accurately predict MCI-to-dementia conversion time by using easily available clinical data. Methods:The dementia diagnosis often falls between two clinical visits, and such survival outcome is known as interval-censored data. We utilized the semi-parametric model and the random forest model for interval-censored data in conjunction with a variable selection approach to select important measures for predicting the conversion time from MCI to dementia. Two large AD cohort data sets were used to build, validate, and test the predictive model. Results:We found that the semi-parametric model can improve the prediction of the conversion time for patients with MCI-to-dementia conversion, and it also has good predictive performance for all patients. Conclusions:Interval-censored data should be analyzed by using the models that were developed for interval- censored data to improve the model performance.
Keywords: Alzheimer’s disease, interval-censored data, MCI-to-dementia conversion, model selection, random forest, survival data
